Measuring relevance by frequency

Bag of words approach

Get key figures of texts

wc *.txt    # count number of lines, words, characters

Get all word occurrences

Show phrase in context

egrep -ir "data" FOLDER/    # search in all files in given folder

# common egrep arguments:
# -i            search case-insensitive
# -r            search recursively in all subfolders
# --colour      highlight matches
# --context 2   show 2 lines above/below match

Count word occurrences

Get term counts per file

egrep -ci "big data" *.txt  # search in file of type .txt only

# arguments:
# -c count matches per file
# -i match case-insensitive

Word frequencies

Steps of the algorithm

1. split text into one word per line (tokenize)
2. sort words alphabetically
3. count how often each word appears

# piping steps to get word frequencies
cat text.txt | ... > wordfrq.txt

Word frequencies

Steps of the algorithm

1. split text into one word per line (tokenize)
2. sort words alphabetically
3. count how often each word appears

# piping steps to get word frequencies
cat text.txt | tr " " "\n" | sort | uniq -c | sort -h > wordfrq.txt

# explanation of individual steps
tr " " "\n"     # replace spaces with newline 
sort -h         # sort lines alphanumerically
uniq -c         # count repeated lines

Convert stats into dataset

• convert to .tsv file
• useful for further processing
  • e.g., import in Excel

# convert word frequencies into tsv-file
# additional step: replace a sequence of spaces with a tabulator
cat text.txt | tr " " "\n" | sort | uniq -c | sort -h | \
tr -s " " "\t"  > test.tsv  

Two kinds of word frequencies

• absolute frequency
  • = n_occurrences
• relative frequency
  • = n_occurrences / n_total_words
  • allows to compare corpora of different sizes

In-class: Matching and counting I

1. Print the following sentence in your command line using echo.

   echo "There are related computational fields: \
   NLP, Computational Linguistics, and computational text analysis."

2. How many words are in this sentence? Use the pipe operator | to pass the output above to the command wc.

3. Match the words computational and colorize its occurences in the sentence using egrep.

↪️ Continue on the next slide.

In-class: Matching and counting II

4. Get the frequencies of each word in this sentence using tr and other commands.

5. Save the frequencies into a tsv-file, open it in a spreadsheet programm (e.g., Excel, Numbers) and compute the relative frequency per word.

6. Are there some words that, although different, should be considered as the same?

Preprocess your data

refine the results with

• lowercasing
• replacing symbols
• joining lines
• trimming header + footer
• splitting into multiple files
• patterns to remove/extract parts

Searching with patterns

We searched for exact matches until now. But …

How to find all words starting with the letter A?

egrep -r "A\w+" **/*.txt 

egrep -r "[0-9]{1,2}\.[0-9]{1,2}\.[0-9]{4}"

When using patterns?

• finding 🔎
• extracting 🛠️
• removing/cleaning 🗑️
• replacing 🔁

… specific parts in texts

An introduction to Regular Expressions (RegEx)

RegEx builds on two classes of symbols

• literal characters and strings
  • letters, digits, words, phrases, dates etc.
• meta expressions with special meaning
  • e.g., \w represents all alphanumeric characters

Combining RegEx with frequency analysis

Something actually useful! 🤩

# count political areas by looking up words ending with "politik"
egrep -rioh "\w*politik" **/*.txt | sort | uniq -c | sort -h

# count ideologies/concepts by looking up words ending with "ismus"
egrep -rioh "\w*ismus" **/*.txt | sort | uniq -c | sort -h

# arguments:
# -o   ouput only match and not entire line

Quantifiers

Repeat preceding character X times

• * zero or any number
• ? zero or one
• + one or more
• {n}, {min,max} a specified number of times

egrep -r "a+"            # match one or more "a"
egrep -r "e{2}"          # match sequence of two "e"

⚠️ Do not confuse regex with Bash wildcards!

Character sets

• [...] any of the characters between brackets
  • any vowel: [auoei]
  • any digit: [0-9]
  • any uppercased letter: [A-Z]
  • any lowercased letter: [a-z]

egrep -r "[Gg]rüne" # match both `grüne` and `Grüne`

egrep -r "[aeiou]{3}" # match sequences of 3 vowels

Special symbols

• . matches any character (excl. newline)
• \ escapes to match literal
  • \. means the literal . instead of “any symbol”
• \w matches any alpha-numeric character
  • same as [A-Za-z0-9_]
• \s matches any whitespace (space, newline, tab)
  • same as [ \t\n]

# match anything between brackets
egrep -r "\(.*\)"

The power of .* 💪

Match any character any times

In-class: Getting ready

1. Go to the website https://www.swissinfo.ch/ger and copy a few paragraphs of any article.

2. After that, go to the website https://regex101.com/and paste the text into the big white field.

3. Write various regex patterns in the small field to match

   1. all uppercased words (~nouns)
   2. words with exactly 5 characters
   3. all words at the beginning of a sentence (~after a period)
   4. match everything between quotes

   TODO

4. Come up with your own challenge

Additional resources for using Bash

When you look for useful primers on Bash, consider the following resources:

• Tutorial Basic Text Analysis by W. Turkel
• Tutorial Pattern Matching + KWIC by W. Turkel
• Cheatsheet of this course

Additional Resource for using Regex

• online regular expression editor regex101
  • to write and check patterns
• interactive regex tutorial regexone
• introductions to regular expressions
  • A Visual Guide to Regular Expression (highly recommended)
  • Regular Expressions (comprehensive; for digital humanities)

Optional exercise: Get the programmes of Swiss parties

1. Change into your local copy of the GitHub course repository KED2025 and update it with git pull. When you haven’t cloned the repository yet, follow section 5 of the installation guide .
2. You find some party programmes (Grüne, SP, SVP) in ked/materials/data/swiss_party_programmes/txt. Change into that directory using cd.
3. The programmes are provided in plain text which I have extracted from the publicly available PDFs. Have a look at the content of some of these text files using more.

Optional exercise: Analyzing Swiss party programmes

1. Compare the absolute frequencies of single terms or multi-word phrases of your choice (e.g., Ökologie, Sicherheit, Schweiz)…

   • across parties
   • historically within a party

   Use the file names as filter to get various aggregation of the word counts.

2. Pick terms of your interest and look at their contextual use by extracting relevant passages. Does the usage differ across parties or time?